Summary of the Chain Rule

Essential Concepts

  • The chain rule for functions of more than one variable involves the partial derivatives with respect to all the independent variables.
  • Tree diagrams are useful for deriving formulas for the chain rule for functions of more than one variable, where each independent variable also depends on other variables.

Key Equations

  • Chain rule, one independent variable
    [latex]\dfrac{dz}{dt}=\dfrac{\partial z}{\partial x}\cdot\dfrac{dx}{dt}+\dfrac{\partial z}{\partial y}\cdot\dfrac{dy}{dt}[/latex]
  • Chain rule, two independent variables
    [latex]\begin{array}{c} \dfrac{\partial z}{\partial u} &=\dfrac{\partial z}{\partial x}\dfrac{\partial x}{\partial u}+\dfrac{\partial z}{\partial y}\dfrac{\partial y}{\partial u} \\\dfrac{\partial z}{\partial v} &=\dfrac{\partial z}{\partial x}\dfrac{\partial x}{\partial v}+\dfrac{\partial z}{\partial y}\dfrac{\partial y}{\partial v} \end{array}[/latex]
  • Generalized chain rule
    [latex]\dfrac{\partial w}{\partial t_{j}}=\dfrac{\partial w}{\partial x_{1}}\dfrac{\partial x_{1}}{\partial t_{j}}+\dfrac{\partial w}{\partial x_{2}}\dfrac{\partial x_{1}}{\partial t_{j}}+\ldots+\dfrac{\partial w}{\partial x_{m}}\dfrac{\partial x_{m}}{\partial t_{j}}[/latex]

Glossary

generalized chain rule
the chain rule extended to functions of more than one independent variable, in which each independent variable may depend on one or more other variables
intermediate variable
given a composition of functions (e.g., [latex]f\left(x(t),y(t)\right)[/latex]) the intermediate variables are the variables that are independent in the outer function but dependent on other variables as well; in the function [latex]f\left(x(t),y(t)\right)[/latex] the variables [latex]x[/latex] and [latex]y[/latex] are examples of intermediate variables
tree diagram
illustrates and derives formulas for the generalized chain rule, in which each independent variable is accounted for